The Design and Validation of ICN-Enabled Hybrid Unmanned Aerial System

This work presents a measurement study that evaluates a novel Information Centric Networking (ICN)-enabled Hybrid Unmanned Aerial Vehicle (UAV) System called IH-UAS. IH-UAS leverages ICN along with an innovative system model integrating broker-based publish-subscribe message dissemination with a decentralized architecture to form an ad hoc (infrastructure-less) UAS to carry out military missions. The overarching research goal that drives this study is to design a system that pushes decision-making to the UAV swarm on the battlefield such that mission tasks are completed more reliably and in less time than traditional centralized UAV-based missions. We use theoretical and measurement-based analysis to validate the system. Through experiments conducted using a simplified variant of a Coordinated Search and Tracking (CSAT) application in IH-UAS, we demonstrate that IH-UAS performs better than the same application operating in a traditional centralized solution. We also show that the broker placement and the number of brokers are critical to application performance.

I. Introduction

Unmanned Aerial Vehicles (UAVs) provide pivotal assistance for tactical missions. A UAV on the battlefield communicates with a ground control station (GCS) using a beyond line of sight satellite link or a line of sight tactical data link. This system is collectively referred to as an Unmanned Aerial System (UAS). The US Department of Defense (DOD) classifies UAVs into five categories that help identify similar types of UAVs across several dimensions such as size, operational limitations, and physics-based mobility capabilities [1]. A large UAV such as the MQ-9 Reaper is classified as group 5 UAV while a smaller UAV such as the Wasp III is classified as group 1 UAV. Group 1 UAVs are comparatively resource-constrained in terms of computation, memory, communications, and battery power. However, they offer scalable and flexible deployment for battlefield missions in urban areas.